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Competitive Privacy: Secure Analysis on Integrated Sequence Data

  • Raymond Chi-Wing Wong
  • Eric Lo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5982)

Abstract

Sequence data analysis has been extensively studied in the literature. However, most previous work focuses on analyzing sequence data from a single source or party. In many applications such as logistics and network traffic analysis, sequence data comes from more than one source or party. When multiple autonomous organizations collaborate and integrate their sequence data to perform analysis, sensitive business information of individual parties can be easily leaked to the other parties. In this paper, we propose the notion of competitive privacy to model the privacy that should be protected when carrying out data analysis on integrated sequence data. We propose a query restriction algorithm that can reject malicious queries with low auditing overhead. Experimental results show that our proposed method guarantees the protection of competitive privacy with only a significantly small portion of queries being restricted.

Keywords

Secure Analysis Concept Hierarchy Average Sequence Length Ground Node Query Restriction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Raymond Chi-Wing Wong
    • 1
  • Eric Lo
    • 2
  1. 1.Hong Kong University of Science and Technology 
  2. 2.Hong Kong Polytechnic University 

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